Gender Equity Dashboard

Housing data

In this first chunk, let’s read in the Census microdata. Here is some example code on how to read in the data, create new variables to categorize the rows of data into groups, and then summarize the data to create information about Louisville.

Our goal is to create variables for gender, age group, whether someone is a mother, whether someone is married, their level of education, their income, whether they are the head of household, and the number of children they have.

This code chunk will identify which households are homeowners vs. renters (in the homeownership variable) and which households are cost-burdened, meaning they pay more thatn 30% of their income toward rent or a mortgage (in the cost_burden variable).

There are also variables for severe cost burden (households that pay more than half of their income towards housing) and households with severe housing problems (lacking a kitchen, adequate plumbing, or an ample number of rooms for the number of people living there).

load("clean_svybydemog_data.RData")

#Waffle Chart

Homeownership

Trends in Homeownership by gender

temp_df <- H_earntype %>%
  filter(race == 'total',
         var_type == "percent", sex == "total") %>%
  pivot_wider(names_from = "earner_type_d", values_from = "homeownership")

trend(temp_df, 
      multiple_earner:single_male_earner,
      plot_title = "Homeownership by Year", 
      cat = c("Multiple Earners" = "multiple_earner", "Single Female" = "single_fem_earner", "Single Male" = "single_male_earner"),
      pctiles = F,
      y_title = 'Percent',
      rollmean = 3,
      caption_text = 
      "Source: Greater Louisville Project
       Data from GLP analysis of ACS microdata from IPUMS-USA")

Single female homeownership

Ranking

temp_df <- H_earntype %>% 
  filter(earner_type_d == "single_fem_earner",
         var_type == "percent", sex == "total") %>%
  mutate(sex = "total")

ranking(temp_df, 
        'homeownership',
        plot_title = "Single Earner Female Homeownership",
        caption_text = 
      "Source: Greater Louisville Project
       Data from GLP analysis of ACS microdata from IPUMS-USA")

by presence of kids

H_s_Femkids_trend %<>%
  filter(
    var_type == 'percent',
    race == 'total',
    sex == "female") %>%
  pivot_wider(names_from = 'kd_pres', values_from = 'homeownership') %>%
  select(-sex)


trend(H_s_Femkids_trend, 
      kids:no_kids,
      rollmean = 3,
      plot_title = "Female Homeownership by Presence of Children", 
      cat = c("Children" = "kids", "No Children" = "no_kids"), 
      y_title = 'Percent',
      caption_text = 
      "Source: Greater Louisville Project
       Data from GLP analysis of ACS microdata from IPUMS-USA")

Ranking with kids

ranking(H_sinFem_kids,
        'homeownership',
        plot_title = "Single Earner Female Homeownership with Children",
        #title_scale = 0.8,
        caption_text = 
      "Source: Greater Louisville Project
       Data from GLP analysis of ACS microdata from IPUMS-USA")

temp_df <- H_earntype %>%
  filter(earner_type_d == "single_fem_earner",
         var_type == "percent", sex == "total")

trend(filter(temp_df, race != "hispanic"), 
      homeownership, 
      rollmean = 3,
      pctiles = F,
      plot_title = "Single Female Homeownership by Year", 
      cat = 'race', 
      y_title = 'Percent',
      caption_text = 
      "Source Greater Louisville Project
       Data from GLP analysis of ACS microdata from IPUMS-USA")

Homeownership by Race accounting for Children

With Children

df_kids_race <- census_microdata081122 %>%
  group_by(FIPS, year, race, earner_type_d, kd_pres) %>%
  summarize(homeownership = sum(HHWT[homeownership]) / sum(HHWT) * 100, .groups = "drop")

df_kids <- census_microdata081122 %>%
  group_by(FIPS, year, earner_type_d, kd_pres) %>%
  summarize(homeownership = sum(HHWT[homeownership]) / sum(HHWT) * 100, .groups = "drop") %>%
  mutate(race = "total")

df_kids %<>%
  bind_rows(df_kids_race) %>%
  select(FIPS, year, race, earner_type_d, homeownership, kd_pres) %>%
  filter(earner_type_d == "single_fem_earner",
         kd_pres == "kids")

trend(filter(df_kids, race != "hispanic"), 
      homeownership, 
      rollmean = 3,
      pctiles = F,
      plot_title = "Single Female Homeownership by Year with Children", 
      cat = 'race', 
      y_title = 'Percent',
      caption_text = 
      "Source Greater Louisville Project
       Data from GLP analysis of ACS microdata from IPUMS-USA")

Without Children

df_no_kids_race <- census_microdata081122 %>%
  group_by(FIPS, year, race, earner_type_d, kd_pres) %>%
  summarize(homeownership = sum(HHWT[homeownership]) / sum(HHWT) * 100, .groups = "drop")

df_no_kids <- census_microdata081122 %>%
  group_by(FIPS, year, earner_type_d, kd_pres) %>%
  summarize(homeownership = sum(HHWT[homeownership]) / sum(HHWT) * 100, .groups = "drop") %>%
  mutate(race = "total")

df_no_kids %<>%
  bind_rows(df_no_kids_race) %>%
  select(FIPS, year, race, earner_type_d, homeownership, kd_pres) %>%
  filter(earner_type_d == "single_fem_earner",
         kd_pres == "no_kids")

trend(filter(df_no_kids, race != "hispanic"), 
      homeownership, 
      rollmean = 3,
      pctiles = F,
      plot_title = "Single Female Homeownership by Year without Children", 
      cat = 'race', 
      y_title = 'Percent',
      caption_text = 
      "Source Greater Louisville Project
       Data from GLP analysis of ACS microdata from IPUMS-USA")

Single Earner Females in need of Affordable Housing

Cost burden vs. Severe Cost Burden

Severe Cost Burden vs. Severe Housing Problems?

Income

Income Percentiles

#fix formatting
single_earner_pctiles <- lville_2019 %>%
  group_by(sex) %>%
  summarize(
    ten_pct = Hmisc::wtd.quantile(HHINCOME, HHWT, probs = 0.1),
    twenty_five_pct = Hmisc::wtd.quantile(HHINCOME, HHWT, probs = 0.25),
    fifty_pct = Hmisc::wtd.quantile(HHINCOME, HHWT, probs = 0.5),
    seventy_five_pct = Hmisc::wtd.quantile(HHINCOME, HHWT, probs = 0.75),
    ninety_pct = Hmisc::wtd.quantile(HHINCOME, HHWT, probs = 0.9))

library(gt) 

gt(single_earner_pctiles) %>%
  tab_header(title = "Income Percentiles by Sex",
             subtitle = "") %>%
  fmt_currency(columns = vars(ten_pct, twenty_five_pct, fifty_pct, seventy_five_pct,
                              ninety_pct),
                            use_subunits = F) %>%
  cols_label(ten_pct = "10th",
             twenty_five_pct = "25th",
             fifty_pct = "Median",
             seventy_five_pct   = "75th",
             ninety_pct = "90th") %>%
  cols_align(align = "center") %>%
      tab_source_note(
    source_note = md("Source: ACS microdata from IPUMS-USA")) %>%
  opt_row_striping(row_striping = TRUE) %>%
  opt_table_outline() %>%
  tab_options(
    table.font.size = px(12),
    table.width = pct(50)) %>%
  tab_style(
    cell_text(
      font = "Montserrat",
      weight = "bold"), 
    cells_row_groups())

Income Percentiles by Sex
sex	10th	25th	Median	75th	90th
female	$11,200	$25,000	$50,000	$91,000	$152,000
male	$18,000	$37,000	$66,900	$108,400	$170,000
Source: ACS microdata from IPUMS-USA

Income by Earner Gender

Counts

p <- lville_2019 %>% 
  filter(HHINCOME <= cut_95,
         earner_type == "single_earner") %>%
  func_plt_hist_overlay( "sex")

p <- p + glp_graph_theme

p <- p + labs(
  title = "Single Earner Income by Gender",
) +
  
  ylab(" ") +
  
  guides(color = FALSE) + 
  
  facet_wrap(~sex, nrow = 2) +
  
  theme(
  #axis.ticks.x =  element_line(size = 50000),
  strip.text = element_blank()

  )  +

scale_x_continuous(
  breaks = c(50000, 100000, 150000, 200000),
  label = c("$50k", "$100k", "$150k", "$200k")
) +
  scale_y_continuous(labels = scales::comma)

p

percent

temp_df <- lville_2019 %>% 
  filter(HHINCOME <= cut_95,
         earner_type == "single_earner")

  p_percent <- ggplot(temp_df, aes(x=HHINCOME, 
                                   y = (..count..)/sum(..count..),
                                   fill=sex, 
                                   color = sex, 
                                   weight = HHWT)) +
    geom_histogram(alpha=0.5, position = 'identity', binwidth = 10000) +
    scale_fill_manual(values = c("#0E4A99", "#F58021", "#00A9B7")) +
    scale_color_manual(values = c("#0E4A99", "#F58021", "#00A9B7")) +

    labs(fill="") +
    xlab("Household Income") +
    ylab("Percentage") 

p_percent <- p_percent + glp_graph_theme

p_percent <- p_percent + labs(
  title = "Single Earner Income by Gender",
) +
  
  ylab(" ") +
  
  guides(color = FALSE) + 
  
  facet_wrap(~sex, nrow = 2) +
  
  theme(
  #axis.ticks.x =  element_line(size = 50000),
  strip.text = element_blank()

  )  +

scale_x_continuous(
  breaks = c(50000, 100000, 150000, 200000),
  label = c("$50k", "$100k", "$150k", "$200k")
) +
  scale_y_continuous(labels=percent)

p_percent

Single Female Income by Race

All Single Female Earners

##add original faceted graph
sing_fem_inc_race<-census_microdata081122 %>% 
  filter(
    FIPS == "21111",
    year %in% 2016:2019,
    sex == 'female',
    earner_type == 'single_earner',
    HHINCOME <= cut_95) 
  
   sing_fem_inc_race_plt <- sing_fem_inc_race %>%
   ggplot( aes(x=HHINCOME, 
              y = (..count..)/sum(..count..),
               fill=race, 
               color = race, 
               weight = HHWT)) +
    geom_histogram(alpha=0.5, position = 'identity', binwidth = 10000) 


sing_fem_inc_race_plt <- sing_fem_inc_race_plt + facet_wrap(~race, nrow = 2) 

sing_fem_inc_race_plt <- sing_fem_inc_race_plt + glp_graph_theme

sing_fem_inc_race_plt <- sing_fem_inc_race_plt + 
  labs(
  title = "Female Single Earner Income",
) + 
  ylab(" ") +
  xlab("Household Income")
  
  # guides(color = FALSE)

sing_fem_inc_race_plt <- sing_fem_inc_race_plt + 
  
  theme( 
  #axis.ticks.x =  element_line(size = 50000),
  strip.text = element_blank()
  
  )  + 

scale_x_continuous(
  breaks = c(50000, 100000, 150000),
  label = c("$50k", "$100k", "$150k")
) +
  scale_y_continuous(labels = scales::percent)

sing_fem_inc_race_plt <- sing_fem_inc_race_plt +
  
  scale_fill_manual(values = c("#0E4A99", "#F58021","#00A9B7", "#800055")) +
  scale_color_manual(values = c("#0E4A99","#F58021","#00A9B7", "#800055")) 

sing_fem_inc_race_plt

Single Black Female Earners

#need to create four separate tabs...one for each race...using % y axis

black_female_earner <- func_income_by_race("black")
black_female_earner

Single Hispanic Female Earners

hisp_female_earner <- func_income_by_race("hispanic")
hisp_female_earner <- hisp_female_earner + 
  labs(
  title = "Hispanic Female Single Earner Income",
) + 
  scale_fill_manual(values = "#0E4A99") +
  scale_color_manual(values = "#0E4A99")
  
hisp_female_earner

Single White Female Earners

white_female_earner <- func_income_by_race("white")
white_female_earner <- white_female_earner + 
  labs(
  title = "White Female Single Earner Income",
)  + 
  scale_fill_manual(values = "#F58021") +
  scale_color_manual(values = "#F58021")

white_female_earner

Single Other Race Female Earners

other_female_earner <- func_income_by_race("other")
other_female_earner <- other_female_earner + 
  labs(
  title = "Other Female Single Earner Income",
) + 
  scale_fill_manual(values = "#00A9B7") +
  scale_color_manual(values = "#00A9B7")

other_female_earner

Single Female Income by Children

func_income_by_kids <- function(num_kids, living_wage) {



  w <- census_microdata081122 %>% 
  filter(
    FIPS == "21111",
    year %in% 2016:2019,
    sex == 'female',
    NCHILD == num_kids,
    earner_type == 'single_earner',
    HHINCOME <= cut_95) 
  
   w <- w %>%
   ggplot( aes(x=HHINCOME, 
              y = (..count..)/sum(..count..),
              fill = sex,
              group = sex,
               weight = HHWT)) +
    geom_histogram(alpha=0.5, position = 'identity', binwidth = 10000) +
     geom_vline( aes(xintercept = living_wage), linetype = "dashed",  colour="blue", size = 1.5) 
   

#sing_fem_inc_race_plt <- sing_fem_inc_race_plt + facet_wrap(~race, nrow = 2) 

w <- w + glp_graph_theme

w <- w + 
  labs(
  title = "Black Female Single Earner Income",
) + 
  ylab(" ") +
  xlab("Household Income")+
  
  guides(color = FALSE)

w <- w + 
  
  theme( 
  #axis.ticks.x =  element_line(size = 50000),
  strip.text = element_blank()
  
  )  + 
  
scale_x_continuous(
  breaks = c(50000, 100000, 150000),
  label = c("$50k", "$100k", "$150k")
) +
  scale_y_continuous(labels = scales::percent) 
    
  
  return (w)

}

Single Female Earner No Child

#why is color not working?
#still need to add living wage lines

under_liv_wage_0 <- census_microdata081122 %>% 
  filter(
    FIPS == "21111",
    year %in% 2016:2019,
    sex == 'female',
    NCHILD == 0,
    earner_type == 'single_earner') %>%
  group_by(HHINCOME < 30303.98) %>%
  summarize(count = sum(HHWT)) #a little more than half are earning a living wage
#do this for each graphof this type...add info above chunk

no_kids_female_earner <- func_income_by_kids(0, 30303.98)
no_kids_female_earner <- no_kids_female_earner + 
  labs(
  title = "Female Single Earner Income, No Children",
) + 
  scale_fill_discrete(labels = "No Children")

no_kids_female_earner

Single Female Earner With One Child

under_liv_wage_1 <- census_microdata081122 %>% 
  filter(
    FIPS == "21111",
    year %in% 2016:2019,
    sex == 'female',
    NCHILD == 1,
    earner_type == 'single_earner') %>%
  group_by(HHINCOME < 60264.75) %>%
  summarize(count = sum(HHWT))

one_child <- func_income_by_kids(1, 60264.75)
one_child <- one_child + 
  labs(
  title = "Female Single Earner Income, One Child",
) + 
  scale_fill_manual(values = "#800055", labels = "One Child" ) +
  scale_color_manual(values = "#800055") 

one_child

Single Female Earner with Two Children

under_liv_wage_2 <- census_microdata081122 %>% 
  filter(
    FIPS == "21111",
    year %in% 2016:2019,
    sex == 'female',
    NCHILD == 2,
    earner_type == 'single_earner') %>%
  group_by(HHINCOME < 76451.81) %>%
  summarize(count = sum(HHWT))

two_child <- func_income_by_kids(2, 76451.81)
two_child <- two_child + 
  labs(
  title = "Female Single Earner Income, Two Children",
) + 
  scale_fill_manual(values = "#356E39", labels = "Two Children") +
  scale_color_manual(values = "#356E39") 

two_child

Single Female Earner with Three Children

under_liv_wage_3 <- census_microdata081122 %>% 
  filter(
    FIPS == "21111",
    year %in% 2016:2019,
    sex == 'female',
    NCHILD == 3,
    earner_type == 'single_earner') %>%
  group_by(HHINCOME < 101452.61) %>%
  summarize(count = sum(HHWT))

three_child <- func_income_by_kids(3, 101452.61)
three_child <- three_child + 
  labs(
  title = "Female Single Earner Income With Three Children",
) + 
  scale_fill_manual(values = "#CFB94C", labels = "Three Children") +
  scale_color_manual(values = "#CFB94C") 

three_child

Single Female Earners Cost Burden

these_labels <- paste0(dollar(seq(1, 273500, 10000), scale = 0.001, accuracy = 1, suffix = "k"))

cost_burden_sf <- lville_2019 %>% 
  filter(
    sex == 'female',
    earner_type == 'single_earner',
    HHINCOME <= cut_95) %>%
  mutate(
    cost_burden = factor(cost_burden, 
                         levels = rev(c(TRUE, FALSE)), 
                         labels = rev(c("Cost Burdened", "Non Cost Burdened")), 
                         ordered = TRUE),
    inc_bins = cut(HHINCOME, seq(1, 283500, 10000),
                   labels = these_labels) %>%
      factor(levels = these_labels, ordered = TRUE)
    )
    
temp_df <- cost_burden_sf %>%
  group_by(inc_bins, cost_burden) %>%
  summarize(count = sum(HHWT), .groups = "drop") %>%
  complete(inc_bins, cost_burden, fill = list(count = 0)) %>%
  filter(!is.na(inc_bins)) %>%
  group_by(inc_bins) %>%
  mutate(percent = count / sum(count)) %>%
  ungroup() %>%
  filter(cost_burden == "Cost Burdened")
 
temp_df <- temp_df[1:14,]

cost_burden_sf_plot <- ggplot(temp_df, 
       aes(x = inc_bins,
           y = percent,
           group = 1)) +
  geom_line(linetype = "dotted", color="purple", size=3) +
  geom_point(color="purple", size=8)

cost_burden_sf_plot <- cost_burden_sf_plot + glp_graph_theme

cost_burden_sf_plot <- cost_burden_sf_plot + 
  labs(
  title = "Female Single Earner Cost Burden Trend",
) + 
  ylab(" ") +
  xlab("Household Income") +
  
  guides(color = FALSE) +
  
  theme(
  strip.text = element_blank()

  )  +

  scale_color_manual(values = c("#0E4A99")) +
  scale_y_continuous(labels = scales::percent)

cost_burden_sf_plot

#I_CB_earn_trend <- survey_by_demog(census_microdata081122, weight_var = "HHWT", 'cost_burden', other_grouping_vars = c('earner_type'), breakdowns = "sex")

# I_CB_earn_trend <- survey_by_demog(census_microdata081122, weight_var = "HHWT", 'cost_burden', other_grouping_vars = c('earner_type_d'))

I_CB_earn_trend %<>%
  filter(
    var_type == 'percent',
    race == 'total',
    sex == 'total') %>%
  select( -c(sex,race)) %>%
  pivot_wider(names_from = "earner_type_d", values_from = "cost_burden")
  

trend(I_CB_earn_trend, 
      multiple_earner:single_fem_earner:single_male_earner, 
      pctiles = F,
      plot_title = "Cost Burden by Earner Type",
      cat = c("Multiple Earners" = "multiple_earner", "Single Female Earner" = "single_fem_earner", "Single Male Earner" = "single_male_earner"),
      y_title = 'Percent',
      caption_text = 
      "Source Greater Louisville Project
       Data from GLP analysis of ACS microdata from IPUMS-USA")

I_median_earn_age <- lville_2019 %>%
  group_by(age_group, earner_type_d) %>%
  summarize(Med=median(HHINCOME)) 

I_median_earn_age_plot <- ggplot(I_median_earn_age, 
       aes(x=age_group, y=Med, fill = earner_type_d)) + 
  geom_bar(stat="identity", position='dodge') 

I_median_earn_age_plot <- I_median_earn_age_plot + glp_graph_theme

I_median_earn_age_plot <- I_median_earn_age_plot +
  labs(
  title = "Median Earnings by Age Group",
) + 
  ylab("Household Income") +
  xlab("Age Group") +
  
  scale_y_continuous(labels = scales::dollar) +   
  scale_fill_manual(
    values = c("#0E4A99", "#F58021", "#00A9B7"), 
    labels = c("Multiple Earner", "Single Female Earner", "Single Male Earner"))

  
I_median_earn_age_plot

Education

#need to think about this more...maybe just use to focus on how similar ppl without kids are

E_singM_singF <- census_microdata081122 %>% 
  filter(year %in% 2017:2019, 
         earner_type == 'single_earner') %>%
  group_by(sex, educ, kd_pres) %>%
  summarize(n=sum(HHWT, na.rm = TRUE)) %>%
  mutate(
    total = sum(n),
    rate = n/sum(n)*100,
    educ = factor(educ, 
                  levels = rev(c("no_hs", "hs", "some_col", "assoc", "bach","grad")), 
                  ordered = TRUE))


E_singM_singF_plot <- ggplot(E_singM_singF, 
       aes(x=sex, 
           y=rate, 
           fill = educ)) + 
  geom_bar(stat="identity", position = "fill") 


E_singM_singF_plot <- E_singM_singF_plot + facet_wrap(~kd_pres) 
  
E_singM_singF_plot <- E_singM_singF_plot + glp_graph_theme

E_singM_singF_plot <- E_singM_singF_plot + 
  
  theme(
    legend.position = "right"
    ) +

  labs(
  title = "Educational attainment by gender for single earners",
) + 
  ylab(" ") +
  xlab(" ") +
  scale_fill_discrete(labels = c("Graduate","Bachelor", "Associate", "Some College",  "High School", "No High School")) + 
  scale_x_discrete (labels = c("female" = "Female", "male" = "Male")) +
  
  scale_y_continuous(labels = scales::percent)

E_singM_singF_plot

E_singF_race <- lville_2019 %>% 
  filter(
    sex == 'female',
    earner_type == 'single_earner') %>%
  group_by(race, educ) %>%
  summarize(n=sum(HHWT, na.rm = TRUE)) %>%
  mutate(
    total = sum(n),
    rate = n/sum(n)*100,
    educ = factor(educ, 
                  levels = rev(c("no_hs", "hs", "some_col", "assoc", "bach","grad")), 
                  ordered = TRUE))

E_singF_race_plot <- ggplot(E_singF_race, aes(x=race, y=rate, fill=educ)) + 
geom_bar(stat="identity", position='fill')


E_singF_race_plot <- E_singF_race_plot + glp_graph_theme

E_singF_race_plot <- E_singF_race_plot + 
    theme(
    legend.position = "right"
    ) +
  labs(
  title = "Single Female Education Breakdown",
) + 
  ylab(" ") +
  xlab("Race") +
  scale_fill_discrete(labels = c("Graduate","Bachelor", "Associate", "Some College",  "High School", "No High School")) + 
  scale_x_discrete (labels = c("female" = "Female", "male" = "Male")) +
  
  scale_y_continuous(labels = scales::percent)

E_singF_race_plot

Household Age

cost_burden_age_sf %<>% drop_na(cost_burden) #this will need to be run once and then left alone if tweaking graphs

cost_burden_age_sf_plot <- ggplot(cost_burden_age_sf,
       aes(x=age_group, y=HHWT , fill=cost_burden),
        color="#00A9B7") +
geom_bar(stat="identity", position='fill')

cost_burden_age_sf_plot <- cost_burden_age_sf_plot + glp_graph_theme

cost_burden_age_sf_plot <- cost_burden_age_sf_plot + 
    theme(
    legend.position = "right"
    ) +
  labs(
  title = "Cost Burdened Status by Age",
) + 
  ylab(" ") +
  xlab("Race") +
  scale_fill_discrete(labels = c("Non Cost Burdened", "Cost Burdened")) + 
  #scale_x_discrete (labels = c("female" = "Female", "male" = "Male")) +
  
  scale_y_continuous(labels = scales::percent)

cost_burden_age_sf_plot

#x-axis...not legible on Josh's comp

temp_df <- cost_burden_age_sf %>%
  mutate(
    age_group = case_when(
      age %in% 15:19 ~ NA_character_, 
      age %in% 20:29 ~ "20-29", 
      age %in% 30:39 ~ "30-39",  
      age %in% 40:49 ~ "40-49",  
      age %in% 50:59 ~ "50-59",  
      age %in% 60:69 ~ "60-69", 
      age %in% 70:79 ~ "70-79", 
      age >= 80 ~ "80+"))

cost_burden_age_sf_facet_plt <- ggplot(temp_df,
       aes(x=age_group, y=HHWT , fill=cost_burden),
        color="#00A9B7") +
geom_bar(stat="identity", position='fill')+
facet_wrap(~earner_type_d)

cost_burden_age_sf_facet_plt <- cost_burden_age_sf_facet_plt + glp_graph_theme

cost_burden_age_sf_facet_plt <- cost_burden_age_sf_facet_plt + 
    theme(
    legend.position = "right",
    
    strip.text = element_text(size = 40)
    ) +
  labs(
  title = "Cost Burdened Status by Age and Earner Type",
) + 
  ylab(" ") +
  xlab(" ") +
  scale_fill_discrete(labels = c("Non Cost Burdened", "Cost Burdened")) + 
  scale_x_discrete(guide = guide_axis(n.dodge=2)) +

  scale_y_continuous(labels = scales::percent)

cost_burden_age_sf_facet_plt

Earner Types Over Time

earner_trend <- census_microdata081122 %>%
  
  mutate(
    earner_type_d = case_when(
      sex == 'female' & earner_type == 'single_earner' ~ 'single_fem_earner',
      sex == 'male' & earner_type == 'single_earner' ~ 'single_male_earner',
      earner_type == 'multi_earner' ~ 'multiple_earner')
  ) %>% 
  group_by(year, earner_type_d) %>%
  summarize(n=sum(HHWT, na.rm = TRUE)) %>%
  mutate(
    total = sum(n),
    rate = n/sum(n)*100) 
  
earner_trend_plt <- ggplot(earner_trend, 
       aes(x=year, y=rate, fill=earner_type_d),
        color="#00A9B7") + 
geom_bar(stat="identity", position='fill')


earner_trend_plt <- earner_trend_plt + glp_graph_theme

earner_trend_plt <- earner_trend_plt + 
    theme(
    legend.position = "right"
    #strip.text = element_blank()
    ) +
  labs(
  title = "Earner Type Trend"
) + 
  ylab(" ") +
  xlab(" ") +
  scale_fill_discrete(labels = c("Multiple Earner", "Single Female Earner", "Single Male Earner")) + 

  scale_y_continuous(labels = scales::percent)

earner_trend_plt